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analysis, data science, discrete and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and database systems. The department has extensive
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of Physics and Astronomy. AIPAD tackles the above questions by developing two innovative AI algorithms: The first algorithm will infer full SEP pitch-angle distributions (PADs) for spacecraft measurements
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for the concept of optimal transport for inverse problems. Optimal transport is concerned with the mathematical problem of comparing and interpolating distributions of mass, for example probability distributions
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developed by the project partners will be based on two key technologies: machine learning algorithms that generate artificial yet realistic data points (synthetic health data) and secure multi-party
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that are of practical interest. The postdoctoral researcher will contribute to the theoretical foundations of inverse problems involving wave phenomena, develop cutting-edge computational algorithms, and apply
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advanced computational approaches to map the diversity, dynamics, and tissue distribution of viruses in the human body. We investigate how these viral communities influence disease susceptibility and
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methodology for analysing long-term spatially structured data sets within a joint species distribution modelling framework. For more information on REC, please see https://www2.helsinki.fi/en/researchgroups
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distribution modelling framework. For more information on REC, please see https://www2.helsinki.fi/en/researchgroups/research-centre-for-ecological-change/ A POSTDOCTORAL RESEARCHER POSITION IN BIODIVERSITY
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. Design, test, and document computational frameworks that combine 4D point cloud data, geospatial analysis, and advanced ML/DL algorithms. Integrate dynamic environmental datasets into immersive and
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combine cutting-edge wet-lab experimentation with advanced computational approaches to map the diversity, dynamics, and tissue distribution of viruses in the human body. We investigate how these viral